Enhanced inversion imaging

ABSTRACT

Data filtering and processing techniques for generating improved wellbore resistivity maps are contemplated. In some aspects, a process of the disclosed technology includes steps for receiving a plurality of measurement sets, wherein each measurement set includes electromagnetic field data associated with a geologic formation, performing an inversion on each of the plurality of measurement sets to generate a corresponding plurality of formation profiles, and applying a filter to each of the formation profiles to generate a plurality of profile clusters. In some aspects, the process can further include steps for selecting a representative cluster from among the profile clusters for use in generating a wellbore resistivity map. Systems and machine-readable media are also provided.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a national stage entry of PCT/US2019/032533 filedMay 15, 2019, said application is expressly incorporated herein in itsentirety.

TECHNICAL FIELD

The present disclosure pertains to formation evaluation and inparticular, to the use of data learning and filtering methods to aidhigh resolution profile generation to facilitate geosteering.

BACKGROUND

In drilling wells for oil and gas exploration, understanding thestructure and properties of the associated geological formation providesinformation to aid such exploration. The collection of informationrelating to formation properties and conditions downhole is commonlyreferred to as “logging,” and can be performed during the drillingprocess.

Various measurement tools exist for use in wireline logging and loggingwhile drilling (LWD). One such tool is an electromagnetic (EM)resistivity tool. A typical resistivity tool includes one or moreantennas for transmitting electromagnetic signals into the formation andone or more antennas for receiving a formation response. When operatedat low frequencies, the resistivity tool may be called an “induction”tool, and at a high-frequencies may be called an electromagnetic wavepropagation tool. Though the physical phenomena that dominate themeasurement can vary with frequency, the operating principles for thetool are consistent. In some cases, the amplitude and/or phase of thereceived signals are compared to the amplitude and/or phase of thetransmitted signals to measure formation resistivity. In other cases,the amplitude and/or phase of the received signals are compared to eachother to measure the formation resistivity.

When plotted as a function of depth or tool position in the borehole,the resistivity tool measurements are termed “logs” or “resistivitylogs.” Such logs can provide indications of hydrocarbon concentrationsand other information useful to drillers and completion engineers. Inparticular, azimuthally-sensitive logs can provide information usefulfor steering the drilling assembly e.g., to facilitate geosteering.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above-recited and otheradvantages and features of the disclosure can be obtained, a moreparticular description of the principles briefly described above will berendered by reference to specific embodiments thereof which areillustrated in the appended drawings. Understanding that these drawingsdepict only exemplary embodiments of the disclosure and are not,therefore, to be considered to be limiting of its scope, the principlesherein are described and explained with additional specificity anddetail through the use of the accompanying drawings in which:

FIG. 1 is a schematic diagram of an example logging-while-drilling (LWD)environment;

FIG. 2 shows an illustrative resistivity logging tool that can beimplemented in accordance with some inventive aspects;

FIG. 3 is a process for performing inversion map processing andfiltering;

FIG. 4 schematically illustrates a processing pipeline for generating animproved formation resistivity map, in accordance with some aspects ofthe disclosed technology;

FIG. 5A illustrates an example formation resistivity map that isgenerated without the use of the disclosed filtering/processingtechniques;

FIG. 5B illustrates an example of an improved formation resistivity mapthat is generated using the disclosed filtering/processing techniques;

FIG. 6A illustrates an example formation resistivity map that isgenerated without the use of the disclosed filtering/processingtechniques;

FIG. 6B illustrates an example of an improved formation resistivity mapthat is generated using the disclosed filtering/processing techniques;

FIG. 7 is a schematic diagram of an example system embodiment.

DETAILED DESCRIPTION

The detailed description set forth below is intended as a description ofvarious configurations of the subject technology and is not intended torepresent the only configurations in which the subject technology can bepracticed. The appended drawings are incorporated herein and constitutea part of the detailed description. The detailed description includesspecific details for the purpose of providing a more thoroughunderstanding of the subject technology. However, it will be clear andapparent that the subject technology is not limited to the specificdetails set forth herein and may be practiced without these details. Insome instances, structures and components are shown in block diagramform in order to avoid obscuring the concepts of the subject technology.

Reservoir resistivity maps are frequently used by drillers andcompletion engineers to facilitate geosteering and enhance production.However, conventional resistivity maps are often generated using datawith limited density, and that has been truncated due to transmissionlosses, resulting in jumping boundaries and artificial features. Suchartifacts limit the usability of the maps and make it difficult toexecute geosteering decisions accordingly.

Aspects of the disclosed technology address the foregoing limitations ofconventional resistivity inversion image generation by providing noveldata processing and filtering methods that improve map resolution andboundary continuity. In some aspects, the disclosed processing andfiltering method includes performing filtering and clustering on allinverted formation profiles. Clusters of formation profiles can then becompared with measurement data for validation, and selected on the basis(of low error), for use in generating final wellbore resistivity mapimages.

To illustrate a context for the disclosed systems and methods, FIG. 1shows a well during drilling operations. A drilling platform 2 isequipped with a derrick 4 that supports a hoist 6. Drilling oil and gaswells is carried out by a string of drill pipes connected together by“tool” joints 7 so as to form a drill string 8. Hoist 6 suspends a kelly10 that lowers the drill string 8. Hoist 6 suspends a kelly 10 thatlowers the drill string 8 through rotary table 12. Connected to thelower end of the drill string 8 is a drill bit 14. Bit 14 is rotated anddrilling accomplished by rotating the drill string 8 by use of adownhole motor near the drill bit, or by both methods.

Drilling fluid, termed “mud,” is pumped by mud recirculation equipment16 through supply pipe 18, through drilling kelly 10, down through thedrill string 8 at high pressures and volumes to emerge through nozzlesor jets in drill bit 14. The mud then travels back up the hole via theannulus formed between the exterior of drill string 8 and borehole wall20, through a blowout preventer, and into a mud pit 24 on the surface.On the surface, the drilling mud is cleaned and then recirculated byrecirculation equipment 16.

For logging while drilling (LWD), downhole sensors 26 are located in thedrillstring 8 near the drill bit 14. Sensors 26 include directionalinstrumentation and a modular resistivity tool with tilted antennas fordetecting bed boundaries. The directional instrumentation measures theinclination angle, the horizontal angle, and the azimuthal angle (alsoknown as the rotational or “tool face” angle) of the LWD tools. As iscommonly defined in the art, the inclination angle is the deviation fromvertically downward, the horizontal angle is the angle in a horizontalplane from true North, and the tool face angle is the orientation(rotational about the tool axis) angle from the high side of thewellbore.

In some embodiments, directional measurements are made as follows: athree-axis accelerometer measures the earth's gravitational field vectorrelative to the tool axis and a point on the circumference of the toolcalled the “tool face scribe line.” (The tool face scribe line is drawnon the tool surface is a line parallel to the tool axis.) From thismeasurement, the inclination and tool face angle of the LWD tool can bedetermined. Additionally, a three-axis magnetometer measures the earth'smagnetic field vector in a similar manner. From the combinedmagnetometer and accelerometer data, the horizontal angle of the LWDtool can be determined. In addition, a gyroscope or other form ofinertial sensor can be incorporated to perform position measurements andfurther refined orientation measurements.

In some embodiments, downhole sensors 26 are coupled to a telemetrytransmitter 28 that transmits telemetry signals by modulating the mudflow in drillstring 8. A telemetry receiver 30 is coupled to kelly 10 toreceive transmitted telemetry signals. Other telemetry transmissiontechniques are well-known and may be used. The receiver 30 communicatesthe telemetry to a surface insulation (not shown) that processes andstores the measurements.

As illustrated in FIG. 1, drill bit 14 is shown penetrating a formationhaving a series of layered beds 34 dipping at an angle. A first (x, y,z) coordinate system associated with the sensors 26 is shown, and asecond coordinate system (x″, y″, z″) associated with the beds 32 isshown. The bed coordinate system has the z″ axis perpendicular to thebedding plane, has the y″ axis in a horizontal plane, and has the x″axis pointing “downhill.” The angle between the z-axis of the twocoordinate systems is referred to as the “dip” and is shown in FIG. 1 asthe angle β.

Referring now to FIG. 2, which depicts an example of a multi-subresistivity tool 100. In the illustrated example, tool 100 comprises aset of four subs distributed along a curved borehole trajectory. Thefour subs include a first sub 140, a second sub 150, a third sub 160,and a fourth sub 170. A longitudinal axis of the first sub 140 isapproximately horizontal with respect to the x-y plane. A drill bit 104attaches to the lower end (relative to the bottom of the wellbore) tothe first sub 140. A first coaxial coil antenna 142 wraps around thebody of the first sub 140 near the lower end. The first sub 140 alsoincludes a first tilted coil antenna 144 positioned adjacent to thefirst coaxial coil antenna 142. The first sub 140 includes a secondtilted coil antenna 146 positioned adjacent to the first tilted coilantenna 144. The first sub 140 also includes a third tilted coil antenna148 adjacent to the second tilted coil antenna 146. In some embodiments,each of the coil antennas 142-148 is operated as transmitter antenna. Inother examples, each of the coil antennas 142-148 can be operated aseither receiver or transmitter antenna.

A connecting tubular 112 attaches to the upper end (relative to the topof the wellbore) of the first sub 140. A second sub 150 attaches to theupper end of the connecting tubular 112. The second sub 150 is rotatablewith respect to its longitudinal axis, and the longitudinal axis can beat an angle with respect to the longitudinal axis of the first sub 140.A first coaxial coil antenna 152 wraps around the lower-most side of thesecond sub 150. The second sub 150 also includes a first tilted coilantenna 154 positioned above and adjacent to the first coaxial coilantenna 152. The second sub 150 includes a second tilted coil antenna156 positioned adjacent to the first tilted coil antenna 154. The secondsub 150 also includes a third tilted coil antenna 158 adjacent to thesecond tilted coil antenna 156. In one example, each of the coilantennas 152-158 is operated as receiver antenna. In other examples,each of the coil antennas 152-158 can be operated as either receiver ortransmitter antenna.

A connecting tubular 114 attaches to the upper end (relative to the topof the wellbore) of the second sub 150. A third sub 160 attaches to theupper end of the connecting tubular 114. The third sub 160 is rotatablewith respect to its longitudinal axis, and the longitudinal axis can beat an angle with respect to the longitudinal axis of the second sub 150.A first coaxial coil antenna 162 wraps around the lower-most side of thethird sub 160. The third sub 160 also includes a first tilted coilantenna 164 positioned above and adjacent to the first coaxial coilantenna 162. The third sub 160 includes a second tilted coil antenna 166positioned adjacent to the first tilted coil antenna 164. The third sub160 also includes a third tilted coil antenna 168 adjacent to the secondtilted coil antenna 166. In one example, each of the coil antennas162-168 is operated as receiver antenna. In other examples, each of thecoil antennas 162-168 can be operated as either receiver or transmitterantenna.

A connecting tubular 116 attaches to the upper end (relative to the topof the wellbore) of the third sub 160. A fourth sub 170 attaches to theupper end of the connecting tubular 114. The fourth sub 170 is rotatablewith respect to its longitudinal axis, and the longitudinal axis can beat an angle with respect to the longitudinal axis of the third sub 170.A first coaxial coil antenna 172 wraps around the lower-most side of thefourth sub 170. The fourth sub 170 also includes a first tilted coilantenna 174 positioned above and adjacent to the first coaxial coilantenna 172. The fourth sub 170 includes a second tilted coil antenna176 positioned adjacent to the first tilted coil antenna 174. The fourthsub 170 also includes a third tilted coil antenna 178 adjacent to thesecond tilted coil antenna 176. In one example, each of the coilantennas 172-178 is operated as receiver antenna. In other examples,each of the coil antennas 172-178 can be operated as either receiver ortransmitter antenna.

Additional details regarding the use of wellbore tools for performingformation resistivity measurements and boundary detection calculationsare provided in U.S. Pat. No. 7,659,722, filed Aug. 8, 2007, which isincorporated by reference herein in its entirety.

FIG. 3 illustrates an example process 300 for performing inversion mapprocessing and filtering, according to some aspects of the disclosedtechnology. Process 300 begins with step 302 in which a plurality of(resistivity) measurement sets are received, for example, from a toolsuch as tool 102, discussed above with respect to FIG. 2. In someaspects, each of the measurement sets can correspond with measurementdata collected at different depths/locations along a wellbore path.Although each measurement set may contain data representing resistivitymeasurements in any direction relative to the wellbore path, in someaspects each measurement set corresponds with vertical slices offormation resistivity at different true vertical distance (TVD) depths.

In step 304, an inversion is performed on each of the measurement setsto generate a corresponding plurality of inverted formation profiles.Because each measurement set contains data gathered at a differentlogging point, the corresponding (inverted) formation profiles canrepresent resistivity boundaries for vertical slices at each loggingpoint.

In step 306, one or more filters are applied to each of the formationprofiles to generate a plurality of profile clusters. Profile clusterscontain sets of filtered (inverted) formation profiles. In some aspects,each cluster can contain sets of formation profiles that have beenfiltered with different filter parameters. By way of example, a firstcluster may contain a set of formation profiles that have all beenfiltered using the same filter i.e., a first filter, whereas a secondcluster may contain a set of formation profiles that have all beenfiltered using a different filter, i.e., a second filter.

In some aspects, filtering may be adjusted based on a formation profilebasis. For example, a first (filtered) formation profile and a second(filtered) formation profile belonging to a first cluster may have beenfiltered using different filter parameters. Filter parameteradjustment/selection that is performed for each formation profile, orfor each profile cluster, can be based on various types of priorinformation, such as information for a particular geologic formation ormeasurement depth. It is understood that any type of information or datacan be used to calibrate profile parameters. However, by way of example,filter parameter selection can be based on seismic data and/or offsetlogs.

Additionally, filtering can be performed using different filteringmethods. By way of example, formation profile filtering can be appliedin the frequency-domain or in the time domain. For frequency-domainfiltering, inverted formation profiles are converted to frequency-domainwaveforms (e.g., using a Fourier transform). Frequency domain filteringis then accomplished by multiplying the frequency-domain waveforms bythe selected filter. In some aspects, wide-band frequency filters areused, for example, to eliminate high frequency components from thewaveforms before they are converted back to time-domain data, e.g.,represented by a 2D/3D pixelated space. As discussed above, frequencyfilter parameters, such as band-width, and center frequency etc., may beselected on a per-formation profile bases, or on a cluster-by-clusterbasis, depending on the desired implementation.

For time domain filtering, filter application can be performed directlyin the 2D/3D pixel space of the formation profile. In some aspects,statistical information (e.g., a histogram) regarding a given formationprofile can be used to selectively eliminate pixel/boundary indicationsthat have a low statistical likelihood of representing ground-truthformation geometries. It is understood that other information, such asformation parameters, may be used to tune time-domain filteringparameters. By way of example, formation parameters can include but arenot limited to: formation resistivity, formation dip, formationanisotropy, and/or bed boundary positions, etc.

Once the inverted formation profiles have been filtered into an adequatenumber of profile clusters, then a process of down-selection can begin(step 308), and a specific profile cluster is selected for use ingenerating a wellbore resistivity map. In some aspects, clusterdown-selection is performed by comparing one or more of the profileclusters to the original measurement set (input) data to determine whichprofile cluster most accurately represents the measurement data. In thismanner, the data filtering process improves output map continuity, whilealso ensuring that the selected profile cluster is true to the originalEM measurement data, received in step 302, discussed above.

In some aspects, down-selection is performed by computing a profilecluster error for each profile cluster, wherein the cluster errorcorresponds with an amount/degree of deviation of the cluster (i.e., theformation profiles in the cluster) from the original measurement setdata. Final cluster selection can then be performed by choosing theprofile cluster with the lowest associated error.

In some aspects, one or more subsequent down-selection processing stepscan be performed. By way of example, formation profiles in each profilecluster may be mixed into additional and/or different profile clusters.That is, individual (filtered) formation profiles can be mixed intodifferent clusters/sets to determine which cluster contain the lowesterror when compared to the input measurement set data. In effect,additional mixing/processing increases the potential solution space forfinal profile cluster selection.

FIG. 4 schematically illustrates a processing pipeline 400 that can beimplemented for generating an improved formation resistivity map,according to some aspects of the disclosed technology. As illustrated,pipeline 400 contains input layer 402 that includes one or moremeasurement data sets, i.e., Measurement Set 1 . . . N. Although anynumber of measurement sets are contemplated, the size of the input layeris typically consistent with the number of logging points in whichmeasurement set data is collected.

Measurement set data in input layer 402 is subject to inversionprocessing in layer 404. As discussed above, inversion processingperformed on each measurement set converts the EM measurement data, ofeach set, into an (inverted) formation profile. That is, the output ofprocessing performed at inversion layer 404 is provided to layer 406,which contains plurality of formation profiles, i.e., Formation Profile1 . . . N. As indicated in block 408, each formation profile in layer406 can be represented by a 2D/3D pixelated space. As discussed above,in some approaches, each formation profile represents 2D/3D formationboundary data for a vertical slice of a wellbore environment. However,other slice orientations are contemplated, without departing from thescope of the disclosed technology.

Next, individual formation profiles are filtered using either atime-domain filter (e.g., as applied in processing sequence 410), or afrequency-domain filter (e.g., as applied in processing sequence 412).As discussed above, time-domain processing 410 can include steps forgenerating a statistical summary (e.g., for each formation profile), andapplying the filter directly in the space/pixel domain.

For frequency-domain filtering applied in processing sequence 412, eachof the (inverted) formation profiles are converted to frequency-domainwaveforms, e.g., using a Fourier transform. Frequency filtering is thenaccomplished by applying the desired frequency filter. As discussedabove, wide-band frequency filters can be used, such as Gaussianfilters, for example, to eliminate high frequency components. However,other filter types are contemplated, without departing from the scope ofthe invention. After filtering, the waveforms are converted back totime-domain data, e.g., represented by a 2D/3D pixelated space.

As discussed above, filter selection can be performed on a formationprofile-by-profile basis. For example, a first (filtered) formationprofile and a second (filtered) formation profile belonging to a firstcluster may have been filtered using different parameters. Filterparameter selection can be based on various types of information, suchas information for a particular geologic formation, or measurementdepth. It is understood that any type of information or data can be usedto calibrate profile parameters. However, by way of example, filterparameter selection can be based on seismic data and/or offset logs.

Once the inverted profiles have been filtered, they are sorted into anarray of profile clusters for further processing/down-selection atlayers 416 and 418. Although pipeline 400 illustrates two processinglayers (416, 418) for the formation profiles, it is understood that agreater (or fewer) number of processing layers may be implemented,without departing from the scope of the disclosed technology.

Cluster down-selection performed at layers 416 and/or 418 can beaccomplished comparing one or more of the profile clusters to theoriginal measurement set input data (in layer 402) to determine whichprofile cluster is the most accurate. As discussed above, down-selectioncan be performed by computing a profile cluster error for each profilecluster, wherein the cluster error corresponds with an amount/degree ofdeviation from the original measurement data. Final cluster selectioncan then be performed by choosing the profile cluster with the lowestassociated error, which is provided as output in processing block 420.The output solution of processing block 420, therefore, represents acollection of (filtered) formation profiles that most accuratelyrepresent the original measurement data received at input layer 402.

In some aspects, final output solutions at block 420 can be comparedwith prior geologic data, such as seismic data and/or offset logs,and/or other LWD measurements such as gamma ray images and/or densityimages, in order to update/tune filter parameters, e.g., using feedback422. In this manner, machine-learning can be deployed to improvefiltering on the basis of various wellbore parameters, such as formationlocation and/or other properties.

FIG. 5A illustrates an example formation resistivity map generatedwithout the data filtering/processing of the disclosed technology.Notably, the map of FIG. 5A is marked by many high-frequency spikes anddiscontinuities.

FIG. 5B illustrates an example formation resistivity map that is basedon the same data as that of FIG. 5A, but that has been generated using afiltering/processing method of the disclosed technology. Notably, manyof the high-frequency spikes seen in FIG. 5A are eliminated, resultingin a more continuous and readable formation map.

FIG. 6A illustrates an example formation resistivity map generatedwithout the data filtering/processing of the disclosed technology.Similar to the map of FIG. 5A, the formation map of FIG. 6A is marked bymany high-frequency spikes and discontinuities.

FIG. 6B illustrates an example formation resistivity map that is basedon the same data as that of FIG. 6A, but that has been generated using afiltering/processing method of the disclosed technology.

FIG. 7 illustrates an exemplary computing system for use with exampletools and systems (e.g., tool 102). The more appropriate embodiment willbe apparent to those of ordinary skill in the art when practicing thepresent technology. Persons of ordinary skill in the art will alsoreadily appreciate that other system embodiments are possible.

Specifically, FIG. 7 illustrates system architecture 700 wherein thecomponents of the system are in electrical communication with each otherusing a bus 705. System architecture 700 can include a processing unit(CPU or processor) 710, as well as a cache 712, that are variouslycoupled to system bus 705. Bus 705 couples various system componentsincluding system memory 715, (e.g., read only memory (ROM) 720 andrandom access memory (RAM) 735), to processor 710. System architecture700 can include a cache of high-speed memory connected directly with, inclose proximity to, or integrated as part of the processor 710. Systemarchitecture 700 can copy data from the memory 715 and/or the storagedevice 730 to the cache 712 for quick access by the processor 710. Inthis way, the cache can provide a performance boost that avoidsprocessor 710 delays while waiting for data. These and other modules cancontrol or be configured to control the processor 710 to perform variousactions. Other system memory 715 may be available for use as well.Memory 715 can include multiple different types of memory with differentperformance characteristics. Processor 710 can include anygeneral-purpose processor and a hardware module or software module, suchas module 1 (732), module 2 (734), and module 3 (736) stored in storagedevice 730, configured to control processor 710 as well as aspecial-purpose processor where software instructions are incorporatedinto the actual processor design. Processor 710 may essentially be acompletely self-contained computing system, containing multiple cores orprocessors, a bus, memory controller, cache, etc. A multi-core processormay be symmetric or asymmetric.

To enable user interaction with the computing system architecture 700,input device 745 can represent any number of input mechanisms, such as amicrophone for speech, a touch-sensitive screen for gesture or graphicalinput, keyboard, mouse, motion input, and so forth. An output device 742can also be one or more of a number of output mechanisms. In someinstances, multimodal systems can enable a user to provide multipletypes of input to communicate with the computing system architecture700. The communications interface 740 can generally govern and managethe user input and system output. There is no restriction on operatingon any particular hardware arrangement and therefore the basic featureshere may easily be substituted for improved hardware or firmwarearrangements as they are developed.

Storage device 730 is a non-volatile memory and can be a hard disk orother types of computer readable media which can store data that areaccessible by a computer, such as magnetic cassettes, flash memorycards, solid state memory devices, digital versatile disks, cartridges,random access memories (RAMs) 735, read only memory (ROM) 720, andhybrids thereof.

Storage device 730 can include software modules 732, 734, 736 forcontrolling the processor 710. Other hardware or software modules arecontemplated. The storage device 730 can be connected to the system bus705. In one aspect, a hardware module that performs a particularfunction can include the software component stored in acomputer-readable medium in connection with the necessary hardwarecomponents, such as the processor 710, bus 705, output device 742, andso forth, to carry out various functions of the disclosed technology.

Embodiments within the scope of the present disclosure may also includetangible and/or non-transitory computer-readable storage media ordevices for carrying or having computer-executable instructions or datastructures stored thereon. Such tangible computer-readable storagedevices can be any available device that can be accessed by a generalpurpose or special purpose computer, including the functional design ofany special purpose processor as described above. By way of example, andnot limitation, such tangible computer-readable devices can include RAM,ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storageor other magnetic storage devices, or any other device which can be usedto carry or store desired program code in the form ofcomputer-executable instructions, data structures, or processor chipdesign. When information or instructions are provided via a network oranother communications connection (either hardwired, wireless, orcombination thereof) to a computer, the computer properly views theconnection as a computer-readable medium. Thus, any such connection isproperly termed a computer-readable medium. Combinations of the aboveshould also be included within the scope of the computer-readablestorage devices.

Computer-executable instructions include, for example, instructions anddata which cause a general-purpose computer, special purpose computer,or special purpose processing device to perform a certain function orgroup of functions. Computer-executable instructions also includeprogram modules that are executed by computers in stand-alone or networkenvironments. Generally, program modules include routines, programs,components, data structures, objects, and the functions inherent in thedesign of special-purpose processors, etc. that perform particular tasksor implement particular abstract data types. Computer-executableinstructions, associated data structures, and program modules representexamples of the program code means for executing steps of the methodsdisclosed herein. The particular sequence of such executableinstructions or associated data structures represents examples ofcorresponding acts for implementing the functions described in suchsteps.

Other embodiments of the disclosure may be practiced in networkcomputing environments with many types of computer systemconfigurations, including personal computers, hand-held devices,multi-processor systems, microprocessor-based or programmable consumerelectronics, network PCs, minicomputers, mainframe computers, and thelike. Embodiments may also be practiced in distributed computingenvironments where tasks are performed by local and remote processingdevices that are linked (either by hardwired links, wireless links, orby a combination thereof) through a communications network. In adistributed computing environment, program modules may be located inboth local and remote memory storage devices.

The various embodiments described above are provided by way ofillustration only and should not be construed to limit the scope of thedisclosure. For example, the principles herein apply equally tooptimization as well as general improvements. Various modifications andchanges may be made to the principles described herein without followingthe example embodiments and applications illustrated and describedherein, and without departing from the spirit and scope of thedisclosure. Claim language reciting “at least one of” a set indicatesthat one member of the set or multiple members of the set satisfy theclaim.

STATEMENTS OF THE DISCLOSURE

Statement 1: a method for generating a wellbore resistivity map,comprising: receiving a plurality of measurement sets, wherein eachmeasurement set comprises electromagnetic field data associated with ageologic formation; performing an inversion on each of the plurality ofmeasurement sets to generate a corresponding plurality of formationprofiles; applying a first filter to each of the formation profiles togenerate a first profile cluster; applying a second filter to each ofthe formation profiles to generate a second profile cluster; selecting arepresentative cluster from among the first profile cluster and thesecond profile cluster for use in generating a wellbore resistivity map.

Statement 2: the system of statement 1, wherein selecting therepresentative cluster further comprises: comparing the first profilecluster to the plurality of measurement sets to determine a firstprofile cluster error; comparing the second profile cluster to theplurality of measurement sets to determine a second profile clustererror; and selecting the representative cluster based on the firstprofile cluster error and the second profile cluster error.

Statement 3: the method of any of statements 1-2, wherein theelectromagnetic field data for each measurement set is associated withdifferent depths in the geologic formation.

Statement 4: the method of any of statements 1-3, wherein the firstfilter is selected based on one or more properties of the geologicformation.

Statement 5: the method of any of statements 1-4, wherein applying thefirst filter to each of the formation profiles further comprisesadjusting the first filter based on a collection depth correspondingwith each of the formation profiles.

Statement 6: the method of any of statements 1-5, wherein the firstfilter comprises one or more filter parameters that are selected basedon seismic data or offset logs.

Statement 7: the method of any of statements 1-6, wherein applying thefirst filter to each of the formation profiles to generate the firstprofile cluster further comprises: converting each of the formationprofiles into a frequency-domain to produce a corresponding plurality offrequency profiles; and applying a frequency filter to each of thefrequency profiles.

Statement 8: a system for generating a wellbore resistivity map, thesystem comprising: one or more processors; and a non-transitory memorycoupled to the one or more processors, wherein the memory comprisesinstruction configured to cause the processors to perform operationsfor: receiving a plurality of measurement sets, wherein each measurementset comprises electromagnetic field data associated with a geologicformation; performing an inversion on each of the plurality ofmeasurement sets to generate a corresponding plurality of formationprofiles; applying a first filter to each of the formation profiles togenerate a first profile cluster; applying a second filter to each ofthe formation profiles to generate a second profile cluster; selecting arepresentative cluster from among the first profile cluster and thesecond profile cluster for use in generating a wellbore resistivity map.

Statement 9: the system of statement 8, wherein selecting therepresentative cluster further comprises: comparing the first profilecluster to the plurality of measurement sets to determine a firstprofile cluster error; comparing the second profile cluster to theplurality of measurement sets to determine a second profile clustererror; and selecting the representative cluster based on the firstprofile cluster error and the second profile cluster error.

Statement 10: the system of any of statements 8-9, wherein theelectromagnetic field data for each measurement set is associated withdifferent depths in the geologic formation.

Statement 11: the system of any of statements 9-10, wherein the firstfilter is selected based on one or more properties of the geologicformation.

Statement 12: the system of any of statements 9-11, wherein applying thefirst filter to each of the formation profiles further comprisesadjusting the first filter based on a collection depth correspondingwith each of the formation profiles.

Statement 13: the system of any of statements 9-12, wherein the firstfilter comprises one or more filter parameters that are selected basedon seismic data or offset logs.

Statement 14: the system of any of statements 9-13, wherein applying thefirst filter to each of the formation profiles to generate the firstprofile cluster further comprises: converting each of the formationprofiles into a frequency-domain to produce a corresponding plurality offrequency profiles; and applying a frequency filter to each of thefrequency profiles.

Statement 15: a tangible, non-transitory, computer-readable media havinginstructions encoded thereon, the instructions, when executed by aprocessor, are operable to perform operations for: receiving a pluralityof measurement sets, wherein each measurement set compriseselectromagnetic field data associated with a geologic formation;performing an inversion on each of the plurality of measurement sets togenerate a corresponding plurality of formation profiles; applying afirst filter to each of the formation profiles to generate a firstprofile cluster; applying a second filter to each of the formationprofiles to generate a second profile cluster; selecting arepresentative cluster from among the first profile cluster and thesecond profile cluster for use in generating a wellbore resistivity map.

Statement 16: the tangible, non-transitory, computer-readable media ofstatement 15, wherein selecting the representative cluster furthercomprises: comparing the first profile cluster to the plurality ofmeasurement sets to determine a first profile cluster error; comparingthe second profile cluster to the plurality of measurement sets todetermine a second profile cluster error; and selecting therepresentative cluster based on the first profile cluster error and thesecond profile cluster error.

Statement 17: the tangible, non-transitory, computer-readable media ofany of statements 15-16, wherein the electromagnetic field data for eachmeasurement set is associated with different depths in the geologicformation.

Statement 18: the tangible, non-transitory, computer-readable media ofany of statements 15-17, wherein the first filter is selected based onone or more properties of the geologic formation.

Statement 19: the tangible, non-transitory, computer-readable media ofany of statements 15-18, wherein applying the first filter to each ofthe formation profiles further comprises adjusting the first filterbased on a collection depth corresponding with each of the formationprofiles.

Statement 20: the tangible, non-transitory, computer-readable media ofany of statements 15-19, wherein the first filter comprises one or morefilter parameters that are selected based on seismic data or offsetlogs.

What is claimed is:
 1. A method for generating a wellbore resistivitymap, comprising: receiving a plurality of measurement sets, wherein eachmeasurement set comprises electromagnetic field data associated with ageologic formation; performing an inversion on each of the plurality ofmeasurement sets to generate a corresponding plurality of formationprofiles; applying a first filter to each of the formation profiles togenerate a first profile cluster; applying a second filter to each ofthe formation profiles to generate a second profile cluster; selecting arepresentative cluster from among the first profile cluster and thesecond profile cluster; and generating a wellbore resistivity map toeither or both facilitate geosteering and enhance production inassociation with the geologic formation, wherein the wellboreresistivity map is generated from the representative cluster that isselected from the first profile cluster and the second profile cluster.2. The method of claim 1, wherein selecting the representative clusterfurther comprises: comparing the first profile cluster to the pluralityof measurement sets to determine a first profile cluster error;comparing the second profile cluster to the plurality of measurementsets to determine a second profile cluster error; and selecting therepresentative cluster based on the first profile cluster error and thesecond profile cluster error.
 3. The method of claim 1, wherein theelectromagnetic field data for each measurement set is associated withdifferent depths in the geologic formation.
 4. The method of claim 1,wherein the first filter is selected based on one or more properties ofthe geologic formation.
 5. The method of claim 1, wherein applying thefirst filter to each of the formation profiles further comprisesadjusting the first filter based on a collection depth correspondingwith each of the formation profiles.
 6. The method of claim 1, whereinthe first filter comprises one or more filter parameters that areselected based on seismic data or offset logs.
 7. The method of claim 1,wherein applying the first filter to each of the formation profiles togenerate the first profile cluster further comprises: converting each ofthe formation profiles into a frequency-domain to produce acorresponding plurality of frequency profiles; and applying a frequencyfilter to each of the frequency profiles.
 8. A system for generating awellbore resistivity map, the system comprising: one or more processors;and a non-transitory memory coupled to the one or more processors,wherein the memory comprises instruction configured to cause theprocessors to perform operations for: receiving a plurality ofmeasurement sets, wherein each measurement set comprises electromagneticfield data associated with a geologic formation; performing an inversionon each of the plurality of measurement sets to generate a correspondingplurality of formation profiles; applying a first filter to each of theformation profiles to generate a first profile cluster; applying asecond filter to each of the formation profiles to generate a secondprofile cluster; selecting a representative cluster from among the firstprofile cluster and the second profile cluster; and generating awellbore resistivity map to either or both facilitate geosteering andenhance production in association with the geologic formation, whereinthe wellbore resistivity map is generated from the representativecluster that is selected from the first profile cluster and the secondprofile cluster.
 9. The system of claim 8, wherein selecting therepresentative cluster further comprises: comparing the first profilecluster to the plurality of measurement sets to determine a firstprofile cluster error; comparing the second profile cluster to theplurality of measurement sets to determine a second profile clustererror; and selecting the representative cluster based on the firstprofile cluster error and the second profile cluster error.
 10. Thesystem of claim 8, wherein the electromagnetic field data for eachmeasurement set is associated with different depths in the geologicformation.
 11. The system of claim 8, wherein the first filter isselected based on one or more properties of the geologic formation. 12.The system of claim 8, wherein applying the first filter to each of theformation profiles further comprises adjusting the first filter based ona collection depth corresponding with each of the formation profiles.13. The system of claim 8, wherein the first filter comprises one ormore filter parameters that are selected based on seismic data or offsetlogs.
 14. The system of claim 8, wherein applying the first filter toeach of the formation profiles to generate the first profile clusterfurther comprises: converting each of the formation profiles into afrequency-domain to produce a corresponding plurality of frequencyprofiles; and applying a frequency filter to each of the frequencyprofiles.
 15. A tangible, non-transitory, computer-readable media havinginstructions encoded thereon, the instructions, when executed by aprocessor, are operable to perform operations for: receiving a pluralityof measurement sets, wherein each measurement set compriseselectromagnetic field data associated with a geologic formation;performing an inversion on each of the plurality of measurement sets togenerate a corresponding plurality of formation profiles; applying afirst filter to each of the formation profiles to generate a firstprofile cluster; applying a second filter to each of the formationprofiles to generate a second profile cluster; selecting arepresentative cluster from among the first profile cluster and thesecond profile cluster; and generating a wellbore resistivity map toeither or both facilitate geosteering and enhance production inassociation with the geologic formation, wherein the wellboreresistivity map is generated from the representative cluster that isselected from the first profile cluster and the second profile cluster.16. The tangible, non-transitory, computer-readable media of claim 15,wherein selecting the representative cluster further comprises:comparing the first profile cluster to the plurality of measurement setsto determine a first profile cluster error; comparing the second profilecluster to the plurality of measurement sets to determine a secondprofile cluster error; and selecting the representative cluster based onthe first profile cluster error and the second profile cluster error.17. The tangible, non-transitory, computer-readable media of claim 15,wherein the electromagnetic field data for each measurement set isassociated with different depths in the geologic formation.
 18. Thetangible, non-transitory, computer-readable media of claim 15, whereinthe first filter is selected based on one or more properties of thegeologic formation.
 19. The tangible, non-transitory, computer-readablemedia of claim 15, wherein applying the first filter to each of theformation profiles further comprises adjusting the first filter based ona collection depth corresponding with each of the formation profiles.20. The tangible, non-transitory, computer-readable media of claim 15,wherein the first filter comprises one or more filter parameters thatare selected based on seismic data or offset logs.